Neural Network-based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

• Published in: International journal of control, automation, and systems Vol. 20; no. 6; pp. 2021 - 2031
• Main Authors: Zhu, Chenglong, Liu, Rui, Li, Baomin, Xia, Jianwei, Zhang, Na
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.06.2022; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Adaptive control; Asymptotic properties; Control; Control systems design; Control theory; Controllers; Engineering; Mechatronics; Neural networks; Nonlinear control; Nonlinear systems; Regular Papers; Robotics; Tracking control; Tracking errors; 제어계측공학; unknown control directions; Event-triggered control; neural network technique; switched nonlinear systems
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-021-0859-5

In this paper, an adaptive event-triggered asymptotic tracking control problem is addressed for switched nonlinear systems with unknown control directions. In existing control schemes, the proposed controller is not directly aimed at the original system, which affects the control performance. Different from the existing control schemes, based on the original system, an event-triggered control law is constructed in this paper. The proposed event-triggered controller guarantees that the tracking error ς 1 asymptotically converges to the origin. Finally, the effectiveness of the proposed controller design scheme is proved by simulation examples.